132 lines
3.8 KiB
Python
132 lines
3.8 KiB
Python
# ==========================================
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# 练习1：图像矩阵操作 (3×3灰度图像)
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# ==========================================
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import numpy as np
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# 定义3×3灰度图像
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print("===== 练习1开始 =====")
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image = np.array([
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[100, 150, 200],
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[80, 120, 180],
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[60, 90, 140]
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], dtype=np.uint8)
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print("原图: ")
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print(image)
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# 1. 变暗20：每个像素值减20
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image_dark = image - 20
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print("\n变暗20后: ")
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print(image_dark)
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# 2. 裁剪左上角：保留2×2区域
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image_crop = image[0:2, 0:2]
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print("\n裁剪左上角后: ")
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print(image_crop)
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# 3. 水平翻转：使用np.fliplr()
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image_flip = np.fliplr(image)
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print("\n水平翻转后: ")
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print(image_flip)
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print("===== 练习1结束 =====\n")
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# ==========================================
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# 练习2：看图写代码 (4×4图像矩阵)
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# ==========================================
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print("===== 练习2开始 =====")
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# 定义4×4图像矩阵
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img = np.array([
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[255, 255, 0, 0],
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[255, 255, 0, 0],
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[0, 0, 255, 255],
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[0, 0, 255, 255]
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], dtype=np.uint8)
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print("原始4x4图像:")
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print(img)
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# 1. 统计白色(255)和黑色(0)像素数量
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white_count = np.sum(img == 255)
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black_count = np.sum(img == 0)
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print(f"\n白色像素(255)数量: {white_count}")
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print(f"黑色像素(0)数量: {black_count}")
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# 2. 水平翻转并打印
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img_flipped = np.fliplr(img)
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print("\n水平翻转后的图像:")
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print(img_flipped)
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# 3. 逆时针旋转90度 (转置 + 上下翻转)
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# 方法：先转置(.T)，再上下翻转(np.flipud)
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img_rotated = np.flipud(img.T)
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print("\n逆时针旋转90度后的图像:")
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print(img_rotated)
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print("===== 练习2结束 =====\n")
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# ==========================================
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# 练习3：特征向量计算
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# ==========================================
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print("===== 练习3开始 =====")
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# 定义两个特征图
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feature_map1 = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
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feature_map2 = np.array([[1, 1, 1], [1, 0, 0], [1, 0, 0]])
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# 展平为向量
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vector1 = feature_map1.flatten()
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vector2 = feature_map2.flatten()
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print("feature_map1 展平向量 vector1: ", vector1)
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print("feature_map2 展平向量 vector2: ", vector2)
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# 1. 计算欧几里得距离
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# 公式: sqrt(sum((a-b)^2))
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euclidean_dist = np.linalg.norm(vector1 - vector2)
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print(f"\nvector1 和 vector2 的欧几里得距离: {euclidean_dist:.4f}")
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# 2. 计算余弦相似度
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# 公式: dot(a,b) / (||a|| * ||b||)
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cos_sim = np.dot(vector1, vector2) / (np.linalg.norm(vector1) * np.linalg.norm(vector2))
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print(f"vector1 和 vector2 的余弦相似度: {cos_sim:.4f}")
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print("===== 练习3结束 =====\n")
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# ==========================================
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# 练习4：文本向量化 (预习挑战)
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# ==========================================
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print("===== 练习4开始 =====")
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# 原始词汇表
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vocab = ["Python", "学习", "数据", "人工智能", "编程"]
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# 两句话
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doc1 = "Python学习编程"
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doc2 = "Python人工智能数据"
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# 定义向量化函数
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def text_to_vector(text, vocab):
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words = text.split()
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vector = np.zeros(len(vocab))
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for i, word in enumerate(vocab):
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vector[i] = words.count(word)
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return vector
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# 生成向量
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v1 = text_to_vector(doc1, vocab)
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v2 = text_to_vector(doc2, vocab)
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print("doc1向量: ", v1)
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print("doc2向量: ", v2)
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# 2. 计算余弦相似度
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cos_sim_text = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
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print(f"\ndoc1 和 doc2 的余弦相似度: {cos_sim_text:.4f}")
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# 3. 修改vocab，加入"机器"，测试doc3 = "机器学习"
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print("\n--- 修改词汇表并测试 doc3 ---")
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new_vocab = ["Python", "学习", "数据", "人工智能", "编程", "机器"]
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doc3 = "机器学习"
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v3 = text_to_vector(doc3, new_vocab)
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print(f"新词汇表: {new_vocab}")
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print(f"doc3('{doc3}')的向量: ", v3)
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print("===== 练习4结束 =====") |